Molecular Dynamics and Machine Learning techniques are useful tools in the Computer-Aided Drug Design (CADD) field. Augmenting both helps to gain a better understanding of the protein behavior. Structure-based drug design (SBDD) was integrated with neural networks to increase the efficiency of discovering potent hit compounds. SBDD augmented with SSnet, deep neural network approach, was applied to identify new peptidommetic inhibitors of the spike protein of SARS-COV2 virus. This was achieved by targeting the spike protein as well as the interaction interface between the viral spike protein and the human Angiotensin-Converting Enzyme 2 (ACE2). The molecular dynamics simulations was carried out for the selected compounds to provide additional insight into the binding with these two target proteins. We anticipate that the integration of Molecular Dynamics and Machine Learning techniques will provide more leverage for various efforts in CADD.

Protein dynamics and machine learning in computer-aided drug design

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.